A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery

A Submonthly Surface Water Classification Framework via Gap-Fill Imputation and Random Forest Classifiers of Landsat Imagery

Global surface water classification layers, such as the European Joint Research Centre’s (JRC) Monthly Water History dataset, provide a starting point for accurate and large scale analyses of trends in waterbody extents. On the local scale, there is an opportunity to increase the accuracy and tempor...

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Bibliographic Details
Main Authors: Charles Labuzzetta, Zhengyuan Zhu, Xinyue Chang, Yuyu Zhou
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
land cover classification
machine learning
outlier detection
remote sensing
gap-fill
imputation
Online Access:https://www.mdpi.com/2072-4292/13/9/1742

Internet

https://www.mdpi.com/2072-4292/13/9/1742

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